Dynamics and Robust Control of a Grid-Connected VSC in Multiterminal DC Grids Considering the Instantaneous Power of DC- and AC-Side Filters and DC Grid Uncertainty
Bibliographic record
Abstract
The electric energy sector is moving toward extensive integration of renewable and clean energy resources, energy storage units, and modern loads via highly efficient and flexible multiterminal dc grids integrated within the traditional ac grid infrastructure. A voltage-source converter (VSC) is the main technology enabling the interconnection of dc and ac grids. In such demanding applications, dc-link voltage control is crucial to maintain system stability. However, the dynamics and control of VSCs considering the instantaneous power of both ac- and dc-side filters and dc grid uncertainties are not addressed in the current literature. Furthermore, as shown in this paper, the uncertainty in the effective dc grid parameters, including filter capacitance and dc-side inductance owing to connecting/disconnecting electric devices to/from the dc grid remarkably affects the converter stability and performance. To overcome these difficulties, this paper presents 1) a detailed small-signal model of the dc-link dynamics in grid-connected VSCs considering the instantaneous power of both ac- and dc-side energy storage components, and 2) a robust optimal dc-link voltage controller. The proposed controller ensures excellent tracking performance, robust disturbance rejection, and robust performance against operating point and parameter variation with a simple fixed-parameter controller. A theoretical analysis, comparative simulation studies, and experimental results are presented to show the effectiveness of the proposed control structure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".